There is a growing demand for automated video surveillance (AVS) systems for public safety and security enhancement. While traditional video surveillance methods require constant human attention, automated visual analysis performs real-time monitoring of people, vehicles and other objects, and generates alerts when suspicious persons or abnormal activity are detected. Such automatic analysis significantly increases the effectiveness of the monitoring by reducing the number of human operators needed, thus is crucial for urban surveillance where over thousands of cameras are set up to monitor a large area on the scale of a city.
Abandoned Object Detection (AOD) techniques detect bags, luggage or other objects that may be left unattended in public places, such as airports. A number of techniques have been proposed or suggested for abandoned object detection. See, for example, Y. L. Tian et al., “Real-Time Detection of Abandoned and Removed Objects in Complex Environments,” IEEE Int'l Workshop on Visual Surveillance (2008), incorporated by reference herein. Nonetheless, lighting changes, occlusions and cluttered backgrounds remain technical challenges.
AOD systems typically detect static objects in a scene, using background modeling and subtraction (BGS). However, a number of non-threatening objects are often observed staying static (such as cars stopping at a red light) or near static (pedestrians standing still on the street) for a short period of time. Moreover, temporarily static objects, if not properly handled, would pose serious adverse effects on background subtraction. Generally, traditional BGS approaches such as the Gaussian mixtures model will gradually adapt people that are standing or sitting still into the background, and as a result, AOD techniques based on BGS may confuse the still people with a suspicious object.
Thus, to minimize the false detection of abandoned objects, a need exists for improved techniques for classifying objects as either a non-threatening object or a suspicious object. A further need exists for improved abandoned object detectors that employ pedestrian detection techniques to distinguish abandoned objects from people.